From Agencies to AI Agents: Scaling Pharma Output Without Scaling Risk

Pharma teams are under pressure to do more than ever before.

More content. More updates. More markets. More scrutiny.

At the same time, budgets are tightening and timelines are shrinking. Yet most organizations are still relying on agency models that scale work by adding people, layers, and handoffs. That model was not designed for today’s pace.

This is why many life sciences teams are now exploring Agentic AI and AI Agents as a new way to increase output without increasing risk.

Not by removing humans, but by redesigning how work moves.

Why the Agency Model Breaks Under Scale

Traditional agencies were built to manage complexity through people. Over time, that has created deeply layered workflows.

As Claudia Beqaj, Managing Partner at CI Life, explained:

“The existing agency model is very human dependent and it’s built with a lot of layers and a lot of teams… there are a lot of human touches that need to take place even before the marketer would even see an initial brief.”

Those layers slow everything down. Each handoff adds time, cost, and opportunity for rework. When output increases, risk compounds because more people touch more assets under tighter timelines.

This is especially visible during label changes or regulatory updates, when hundreds of promotional pieces may need small but critical edits.

“Every time there’s a label change… hundreds of pieces need to be updated,” Claudia said. “That takes weeks to months for an agency to complete and it costs a lot of money depending on how big the brand is.”

Scaling output through agencies often means scaling exposure.

What Agentic AI Changes

Agentic AI does not replace compliance or strategy. It replaces the friction between them.

AI Agents are role-based systems trained to execute specific tasks consistently within defined rules. In regulated environments, that means applying the same nomenclature, claims logic, and formatting requirements every time.

Claudia described where agents immediately outperform agencies:

“Some things are just very simple changes… copy editing, ensuring the right nomenclature is used in every instance. An AI agent gives you automated consistency that you don’t always have with the human eye.”

Instead of dozens of people reviewing the same minutiae across versions, agents perform first-pass execution and checks. Humans stay focused on strategy, judgment, and approvals.

If you want to explore how this model could fit inside your own review workflows, you can speak with CI Life here.

Review, Approval, and Risk at Scale

One of the biggest misconceptions about AI Agents is that they weaken compliance. In practice, they often reduce risk by filtering issues earlier.

In today’s model, everything flows into MLR or PRC meetings, even when pieces contain avoidable errors.

Claudia explained how agents change that dynamic:

“If you were able to run pieces through an agent that scans for inaccuracies and flags what’s missing, that would never even get to the review committee. It would be kicked back immediately.”

This matters when scale increases. MLR teams become overwhelmed not because they are slow, but because volume spikes all at once. Agents absorb that pressure upstream.

Compliance does not disappear. It becomes more focused.

What Changes First When Teams Adopt AI Agents

Teams often expect creative work to change first. In reality, time and cost shiftimmediately .

Projects move faster because agents eliminate back-and-forth edits. Budgets change because fewer billable hours are spent on repetitive tasks.

Claudia was clear about where agencies should still play:

“I would rather the agency focus on bigger, more strategic initiatives, more bold creative work as opposed to things like copy editing… small things that wind up costing brands a lot of money.”

Agentic AI does not eliminate agencies overnight. It removes the parts of agency work that create scale risk.

Measuring Success Without Guessing

Success in an agentic model is not abstract.

Claudia summed it up simply:

“The speed and accuracy of outputs… and lower costs.”

Teams can compare:

• Time to completion
• Error rates in review
• Cost per update
• MLR throughput

When those improve together, output is scaling without exposure.

For teams still relying only on agencies, Claudia warned the challenge ahead is unavoidable:

“Companies aren’t going to continue to stand for huge agency fees, especially if they think AI could do it better.”

If you want to understand how CI Life helps teams transition safely from agencies to AI Agents, start a conversation with us here.

And if you have not yet read it, this blog pairs well with our earlier piece on From Agencies to AI Agents: A New Way Pharma Teams Get Work Done, which explores the foundation of Agentic AI in life sciences.

Scaling output no longer has to mean scaling risk. But it does require changing the system, not just the tools.

Source citation

Quotes and insights drawn directly from Claudia Beqaj, Managing Partner, CI Life, February 3, 2026